| treatment | age_group | patient_id | sample | Sequence |
|---|---|---|---|---|
| 0 | 2 | 3 | 160008699_3_0_S5 | 1 |
| 1 | 2 | 3 | 160008699_3_8_S6 | 2 |
| 0 | 2 | 4 | 290001824_4_0_S7 | 3 |
| 1 | 2 | 4 | 290001824_4_8_S8 | 4 |
| 0 | 1 | 17 | 330001842_17_0_S31 | 5 |
| 1 | 1 | 17 | 330001842_17_8_S32 | 6 |
| 0 | 0 | 5 | 470009458_5_0_S9 | 7 |
| 1 | 0 | 5 | 470009458_5_4_S10 | 8 |
| 0 | 1 | 13 | 660009823_13_0_S25 | 9 |
| 1 | 1 | 13 | 660009823_13_8_S26 | 10 |
| 0 | 0 | 11 | 770004766_11_0_S21 | 11 |
| 1 | 0 | 11 | 770004766_11_8_S22 | 12 |
| 0 | 1 | 2 | 830001304_2_0_S3 | 13 |
| 1 | 1 | 2 | 830001304_2_4_S4 | 14 |
| 0 | 2 | 12 | 830002078_12_0_S23 | 15 |
| 1 | 2 | 12 | 830002078_12_8_S24 | 16 |
| 0 | 2 | 9 | 880001252_9_0_S17 | 17 |
| 1 | 2 | 9 | 880001252_9_8_S18 | 18 |
| 0 | 0 | 8 | 940004357_8_0_S15 | 19 |
| 1 | 0 | 8 | 940004357_8_8_S16 | 20 |
| 0 | 1 | 7 | 970002731_7_0_S13 | 21 |
| 1 | 1 | 7 | 970002731_7_4_S14 | 22 |
| 0 | 0 | 10 | 980007758_10_0_S19 | 23 |
| 1 | 0 | 10 | 980007758_10_8_S20 | 24 |
Soft threshold = 16
soft threshold = 16
.
Modules = 29
29 modules in total
.
| Positive modules | Spearman correlation (p-value) |
|---|---|
| lightgreen (152 genes) | 0.14 (0.1) |
| Negative modules | Spearman correlation (p-value) |
|---|---|
| darkred (63 genes) | -0.12 (0.2) |
| midnightblue (303 genes) | -0.1 (0.2) |
Alpha = 1
Nested cross validation
## Tuned lambda value:
## 0.04336461
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.04336 41 1.160 0.22381 10
## 1se 0.20128 8 1.376 0.05708 1
## Non-zero Coefficients:
## ENSG00000152894 ENSG00000084072 ENSG00000058091 ENSG00000112232 ENSG00000173898 ENSG00000101057 ENSG00000026559 ENSG00000168916 ENSG00000134532 ENSG00000166833
| ensembl_gene_id | external_gene_name |
|---|---|
| ENSG00000026559 | KCNG1 |
| ENSG00000058091 | CDK14 |
| ENSG00000084072 | PPIE |
| ENSG00000101057 | MYBL2 |
| ENSG00000112232 | KHDRBS2 |
| ENSG00000134532 | SOX5 |
| ENSG00000152894 | PTPRK |
| ENSG00000166833 | NAV2 |
| ENSG00000168916 | ZNF608 |
| ENSG00000173898 | SPTBN2 |
## Reference
## Predicted 0 1
## 0 10 6
## 1 2 6
## AUC Accuracy Balanced accuracy
## 0.6597222 0.6666667 0.6666667
| sample | 160008699_3_0_S5 | 290001824_4_0_S7 | 330001842_17_0_S31 | 470009458_5_0_S9 | 660009823_13_0_S25 | 770004766_11_0_S21 | 830001304_2_0_S3 | 830002078_12_0_S23 | 880001252_9_0_S17 | 940004357_8_0_S15 | 970002731_7_0_S13 | 980007758_10_0_S19 | 160008699_3_8_S6 | 290001824_4_8_S8 | 330001842_17_8_S32 | 470009458_5_4_S10 | 660009823_13_8_S26 | 770004766_11_8_S22 | 830001304_2_4_S4 | 830002078_12_8_S24 | 880001252_9_8_S18 | 940004357_8_8_S16 | 970002731_7_4_S14 | 980007758_10_8_S20 |
| KCNG1 | 0.65 | 0.89 | 0.51 | 0.55 | 0.22 | 0.13 | 0.14 | 0.32 | 0.29 | 0.09 | 0.22 | 1.01 | 0.94 | 1.28 | 0.93 | 1.03 | 0.13 | 0.26 | 0.37 | 0.30 | 0.48 | 0.60 | 1.09 | 1.19 |
| CDK14 | 6.49 | 7.49 | 3.21 | 4.61 | 3.87 | 3.58 | 7.03 | 2.71 | 7.50 | 3.65 | 5.13 | 5.42 | 5.21 | 8.77 | 5.54 | 5.35 | 8.58 | 6.32 | 10.43 | 2.10 | 5.82 | 6.42 | 6.11 | 6.72 |
| PPIE | 21.15 | 17.39 | 12.59 | 11.87 | 12.76 | 7.84 | 18.19 | 10.75 | 18.17 | 13.71 | 10.27 | 16.09 | 16.44 | 17.77 | 10.86 | 12.52 | 5.29 | 9.87 | 14.59 | 7.85 | 11.21 | 8.37 | 9.80 | 12.64 |
| MYBL2 | 1.22 | 0.89 | 1.86 | 1.61 | 2.09 | 0.80 | 1.46 | 1.09 | 0.95 | 0.77 | 3.31 | 4.64 | 1.04 | 2.54 | 1.99 | 1.90 | 0.76 | 1.07 | 0.99 | 0.16 | 0.77 | 0.85 | 0.28 | 4.22 |
| KHDRBS2 | 0.52 | 0.31 | 0.38 | 0.25 | 0.30 | 0.10 | 0.37 | 0.13 | 0.48 | 0.16 | 0.19 | 0.39 | 0.46 | 0.48 | 0.41 | 0.27 | 0.20 | 0.15 | 0.34 | 0.71 | 0.36 | 0.40 | 0.75 | 0.58 |
| SOX5 | 0.04 | 1.80 | 0.63 | 1.62 | 0.89 | 0.11 | 2.10 | 0.43 | 1.28 | 0.42 | 0.30 | 0.58 | 0.17 | 0.37 | 0.36 | 2.58 | 0.19 | 0.34 | 0.97 | 0.11 | 0.53 | 0.71 | 0.28 | 0.70 |
| PTPRK | 0.74 | 0.58 | 1.01 | 1.07 | 0.91 | 0.44 | 1.95 | 0.60 | 1.38 | 0.93 | 0.51 | 1.50 | 1.37 | 2.68 | 2.35 | 3.05 | 1.00 | 0.76 | 2.48 | 0.59 | 1.72 | 1.78 | 1.39 | 2.82 |
| NAV2 | 0.71 | 0.47 | 0.35 | 0.88 | 0.73 | 0.06 | 1.10 | 0.51 | 0.29 | 1.05 | 0.46 | 0.31 | 0.63 | 0.87 | 0.23 | 0.27 | 0.27 | 0.05 | 0.65 | 0.26 | 0.07 | 1.44 | 0.25 | 0.28 |
| ZNF608 | 0.68 | 1.06 | 0.76 | 1.04 | 0.39 | 0.14 | 1.20 | 0.76 | 0.43 | 0.21 | 1.24 | 0.91 | 0.40 | 0.86 | 0.44 | 0.98 | 0.50 | 0.46 | 0.58 | 0.31 | 0.49 | 0.27 | 0.17 | 1.19 |
| SPTBN2 | 0.65 | 0.36 | 0.24 | 0.27 | 0.20 | 0.01 | 0.41 | 0.70 | 1.61 | 0.07 | 0.11 | 0.44 | 0.05 | 0.19 | 0.09 | 0.21 | 0.28 | 0.10 | 0.13 | 0.03 | 0.19 | 0.36 | 0.69 | 0.06 |
| treatment | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | post | post | post | post | post | post | post | post | post | post | post | post |
| patient_id | 3 | 4 | 17 | 5 | 13 | 11 | 2 | 12 | 9 | 8 | 7 | 10 | 3 | 4 | 17 | 5 | 13 | 11 | 2 | 12 | 9 | 8 | 7 | 10 |
Alpha = 1
Nested cross validation
## Tuned lambda value:
## 0.1392525
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.1393 11 1.505 0.10086 3
## 1se 0.2217 1 1.506 0.01945 0
## Non-zero Coefficients:
## ENSG00000169519 ENSG00000279982 ENSG00000272502
##
## 0 1
## 0 12 0
## 1 0 12
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 8.575264e-01 1.000000e+00 5.000000e-01
## AccuracyPValue McnemarPValue
## 5.960464e-08 NaN
| ensembl_gene_id | external_gene_name |
|---|---|
| ENSG00000169519 | METTL15 |
| ENSG00000272502 | ENSG00000272502 |
| ENSG00000279982 | ENSG00000279982 |
| sample | 160008699_3_0_S5 | 290001824_4_0_S7 | 330001842_17_0_S31 | 470009458_5_0_S9 | 660009823_13_0_S25 | 770004766_11_0_S21 | 830001304_2_0_S3 | 830002078_12_0_S23 | 880001252_9_0_S17 | 940004357_8_0_S15 | 970002731_7_0_S13 | 980007758_10_0_S19 | 160008699_3_8_S6 | 290001824_4_8_S8 | 330001842_17_8_S32 | 470009458_5_4_S10 | 660009823_13_8_S26 | 770004766_11_8_S22 | 830001304_2_4_S4 | 830002078_12_8_S24 | 880001252_9_8_S18 | 940004357_8_8_S16 | 970002731_7_4_S14 | 980007758_10_8_S20 |
| METTL15 | 5.35 | 3.64 | 1.91 | 1.98 | 3.71 | 1.05 | 4.07 | 2.33 | 3.53 | 1.91 | 2.70 | 2.80 | 2.49 | 3.16 | 2.29 | 1.63 | 0.76 | 1.83 | 2.69 | 2.01 | 2.06 | 1.64 | 1.61 | 2.05 |
| ENSG00000272502 | 0.69 | 2.28 | 0.76 | 1.03 | 0.74 | 0.37 | 1.43 | 0.70 | 1.53 | 0.76 | 0.40 | 1.29 | 1.36 | 1.16 | 0.26 | 0.45 | 0.38 | 0.68 | 0.58 | 0.65 | 0.54 | 0.45 | 0.45 | 0.75 |
| ENSG00000279982 | 0.17 | 0.25 | 0.17 | 0.11 | 0.58 | 0.06 | 0.33 | 0.28 | 0.38 | 0.18 | 0.20 | 0.28 | 0.13 | 0.29 | 0.16 | 0.12 | 0.06 | 0.05 | 0.21 | 0.12 | 0.15 | 0.23 | 0.10 | 0.14 |
| treatment | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | post | post | post | post | post | post | post | post | post | post | post | post |
| patient_id | 3 | 4 | 17 | 5 | 13 | 11 | 2 | 12 | 9 | 8 | 7 | 10 | 3 | 4 | 17 | 5 | 13 | 11 | 2 | 12 | 9 | 8 | 7 | 10 |
Alpha = 1
Nested cross validation
## Tuned lambda value:
## 0.1253536
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.1253 14 1.443 0.10450 2
## 1se 0.2295 1 1.495 0.01162 0
## Non-zero Coefficients:
## ENSG00000186073 ENSG00000164120
##
## 0 1
## 0 12 0
## 1 0 12
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 8.575264e-01 1.000000e+00 5.000000e-01
## AccuracyPValue McnemarPValue
## 5.960464e-08 NaN
| ensembl_gene_id | external_gene_name |
|---|---|
| ENSG00000164120 | HPGD |
| ENSG00000186073 | CDIN1 |
| sample | 160008699_3_0_S5 | 290001824_4_0_S7 | 330001842_17_0_S31 | 470009458_5_0_S9 | 660009823_13_0_S25 | 770004766_11_0_S21 | 830001304_2_0_S3 | 830002078_12_0_S23 | 880001252_9_0_S17 | 940004357_8_0_S15 | 970002731_7_0_S13 | 980007758_10_0_S19 | 160008699_3_8_S6 | 290001824_4_8_S8 | 330001842_17_8_S32 | 470009458_5_4_S10 | 660009823_13_8_S26 | 770004766_11_8_S22 | 830001304_2_4_S4 | 830002078_12_8_S24 | 880001252_9_8_S18 | 940004357_8_8_S16 | 970002731_7_4_S14 | 980007758_10_8_S20 |
| HPGD | 4.38 | 3.06 | 0.78 | 1.14 | 1.61 | 2.61 | 4.23 | 2.18 | 4.24 | 2.07 | 1.90 | 3.77 | 1.95 | 3.31 | 0.82 | 1.07 | 1.84 | 2.90 | 2.15 | 1.00 | 1.47 | 1.06 | 0.91 | 2.23 |
| CDIN1 | 1.14 | 2.48 | 1.36 | 1.04 | 1.94 | 0.74 | 2.13 | 1.24 | 1.95 | 1.05 | 1.43 | 1.74 | 1.35 | 1.72 | 1.58 | 0.96 | 0.58 | 0.52 | 1.61 | 0.81 | 0.77 | 0.72 | 0.83 | 1.15 |
| treatment | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | pre | post | post | post | post | post | post | post | post | post | post | post | post |
| patient_id | 3 | 4 | 17 | 5 | 13 | 11 | 2 | 12 | 9 | 8 | 7 | 10 | 3 | 4 | 17 | 5 | 13 | 11 | 2 | 12 | 9 | 8 | 7 | 10 |
## Tuned lambda value:
## 0.02060171
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.02060 29 0.8468 0.2593 11
## 1se 0.09128 13 1.1030 0.1491 9
## Non-zero Coefficients:
## ENSG00000186073 ENSG00000152894 ENSG00000058091 ENSG00000112232 ENSG00000173898 ENSG00000168916 ENSG00000164120 ENSG00000166833 ENSG00000169519 ENSG00000084072 ENSG00000101057
## Reference
## Predicted 0 1
## 0 9 3
## 1 3 9
## AUC Accuracy Balanced accuracy
## 0.875 0.750 0.750
##
## Call:
## glm(formula = formula_str, family = binomial, data = data.frame(final_model_matrix))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.072 355569.691 0 1
## ENSG00000186073 -13.996 159559.523 0 1
## ENSG00000152894 7.460 155528.692 0 1
## ENSG00000058091 34.317 129080.505 0 1
## ENSG00000112232 11.136 596107.470 0 1
## ENSG00000173898 -32.154 390710.815 0 1
## ENSG00000168916 -5.833 276813.334 0 1
## ENSG00000164120 -20.629 866630.487 0 1
## ENSG00000166833 -8.239 173876.233 0 1
## ENSG00000169519 12.471 946415.656 0 1
## ENSG00000084072 -1.015 278030.257 0 1
## ENSG00000101057 -6.527 594384.931 0 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3.3271e+01 on 23 degrees of freedom
## Residual deviance: 4.1900e-10 on 12 degrees of freedom
## AIC: 24
##
## Number of Fisher Scoring iterations: 25
## Implementation: ROI | Solver: lpsolve
## Separation: TRUE
## Existence of maximum likelihood estimates
## (Intercept) ENSG00000186073 ENSG00000152894 ENSG00000058091 ENSG00000112232
## -Inf -Inf Inf Inf Inf
## ENSG00000173898 ENSG00000168916 ENSG00000164120 ENSG00000166833 ENSG00000169519
## -Inf -Inf -Inf Inf -Inf
## ENSG00000084072 ENSG00000101057
## -Inf Inf
## 0: finite value, Inf: infinity, -Inf: -infinity
## bayesglm(formula = formula_str, family = binomial(link = "logit"),
## data = as.data.frame(final_model_matrix))
## coef.est coef.se
## (Intercept) 0.06 0.82
## ENSG00000186073 -0.84 0.87
## ENSG00000152894 1.26 0.92
## ENSG00000058091 1.30 0.91
## ENSG00000112232 1.07 0.74
## ENSG00000173898 -0.77 0.79
## ENSG00000168916 -0.43 0.75
## ENSG00000164120 -0.38 0.78
## ENSG00000166833 -0.37 0.65
## ENSG00000169519 -0.31 0.87
## ENSG00000084072 -0.29 0.84
## ENSG00000101057 -0.35 0.81
## ---
## n = 24, k = 12
## residual deviance = 4.8, null deviance = 33.3 (difference = 28.5)
##
## Call: bayesglm(formula = formula_str, family = binomial(link = "logit"),
## data = as.data.frame(final_model_matrix), method = "detect_separation")
##
## Coefficients:
## (Intercept) ENSG00000186073 ENSG00000152894 ENSG00000058091
## 0.06073 -0.83927 1.26230 1.30375
## ENSG00000112232 ENSG00000173898 ENSG00000168916 ENSG00000164120
## 1.06638 -0.77216 -0.43275 -0.37849
## ENSG00000166833 ENSG00000169519 ENSG00000084072 ENSG00000101057
## -0.36640 -0.30720 -0.29201 -0.35347
##
## Degrees of Freedom: 23 Total (i.e. Null); 12 Residual
## Null Deviance: 33.27
## Residual Deviance: 4.77 AIC: 28.77
Firth’s bias reduction method, equivalent to penalization of the log-likelihood
## logistf(formula = formula_str, data = as.data.frame(final_model_matrix))
##
## Model fitted by Penalized ML
## Coefficients:
## coef se(coef) lower 0.95 upper 0.95 Chisq p
## (Intercept) 0.1467598 0.4644579 -1.1980458 2.3826018 0.06886319 0.7929992
## ENSG00000186073 -0.3129507 0.6202474 -2.3955619 1.2965489 0.20811403 0.6482496
## ENSG00000152894 0.6736023 0.7548255 -1.3659300 3.7205358 0.63266700 0.4263787
## ENSG00000058091 1.1690416 0.8029840 -0.6709524 4.4023020 1.64697932 0.1993706
## ENSG00000112232 0.6541397 0.5899478 -1.5437707 3.0661566 0.97581356 0.3232346
## ENSG00000173898 -0.6883909 0.5775987 -5.4198388 0.4593585 1.34411190 0.2463101
## ENSG00000168916 -0.4959154 0.6773029 -2.9708523 1.3429128 0.37323096 0.5412484
## ENSG00000164120 -0.3078037 0.8503156 -3.5850636 1.9199496 0.10363021 0.7475160
## ENSG00000166833 -0.4104273 0.4879591 -2.0885087 1.2699108 0.54638992 0.4597965
## ENSG00000169519 0.1244505 1.0006652 -4.1150175 2.5163733 0.01109416 0.9161149
## ENSG00000084072 -0.1642066 1.0510126 -2.6306083 3.6718098 0.01928157 0.8895623
## ENSG00000101057 -0.2105312 0.6647076 -3.3545827 1.8218956 0.07317914 0.7867630
## method
## (Intercept) 2
## ENSG00000186073 2
## ENSG00000152894 2
## ENSG00000058091 2
## ENSG00000112232 2
## ENSG00000173898 2
## ENSG00000168916 2
## ENSG00000164120 2
## ENSG00000166833 2
## ENSG00000169519 2
## ENSG00000084072 2
## ENSG00000101057 2
##
## Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
##
## Likelihood ratio test=16.48283 on 11 df, p=0.1241313, n=24
## Wald test = 10.86186 on 11 df, p = 0.4549077
## logistf(formula = formula_str, data = as.data.frame(final_model_matrix),
## method = "detect_separation")
## Model fitted by Penalized ML
## Confidence intervals and p-values by Profile Likelihood
##
## Coefficients:
## (Intercept) ENSG00000186073 ENSG00000152894 ENSG00000058091 ENSG00000112232
## 0.1467598 -0.3129507 0.6736023 1.1690416 0.6541397
## ENSG00000173898 ENSG00000168916 ENSG00000164120 ENSG00000166833 ENSG00000169519
## -0.6883909 -0.4959154 -0.3078037 -0.4104273 0.1244505
## ENSG00000084072 ENSG00000101057
## -0.1642066 -0.2105312
##
## Likelihood ratio test=16.48283 on 11 df, p=0.1241313, n=24
## [1] "AUC (test): 1"
## [1] "Accuracy (test): 1"
Check direction of each gene in two models, all the same
| Modules (size) | Module correlation to treatment | Genes selected by lasso |
|---|---|---|
| lightgreen (152 genes) | Positive | 8 |
| darkred (63 genes) | Negative | 1 |
| midnightblue (303 genes) | Negative | 2 |
| ensembl_gene_id | external_gene_name | |
|---|---|---|
| 1 | ENSG00000058091 | CDK14 |
| 2 | ENSG00000084072 | PPIE |
| 3 | ENSG00000101057 | MYBL2 |
| 4 | ENSG00000112232 | KHDRBS2 |
| 5 | ENSG00000152894 | PTPRK |
| 6 | ENSG00000166833 | NAV2 |
| 7 | ENSG00000168916 | ZNF608 |
| 8 | ENSG00000173898 | SPTBN2 |
| 9 | ENSG00000169519 | METTL15 |
| 10 | ENSG00000164120 | HPGD |
| 11 | ENSG00000186073 | CDIN1 |
Reactome
.
KEGG
.